Register now to attend the virtual ARCH Workshop, "Experimental Design Patterns" hosted by Joshua Garrison Burkhart, Ph.D.
Workshop Description:
Learn to design experimental dataflows for machine learning projects through hands-on exercises using Python tools such as scikit-learn, pandas, PyTorch, and AutoGluon, while identifying practical design patterns and addressing common challenges.
Date/Time: Thursday, March 20, 2025 - 2:00 PM CT/3:00 PM ET
Location: Virtual Workshop. Zoom link will be provided upon registration.
Prerequisites for Workshop:
The session requires a basic understanding of experimental design concepts and experience programming in Python.
Datasets & Tools:
Datasets: Provide sample datasets that illustrate different experimental design scenarios integrated with machine learning challenges.
Tools: Utilize programming environments such as Python (with libraries like scikit-learn and pandas) or R (with packages such as caret and tidyverse). Consider using platforms like Jupyter notebooks to enhance interactivity.
Expected Outcomes:
By the end of this workshop, participants will:
1. Identify and understand common experimental design patterns in the context of machine learning.
2. Gain hands-on experience applying these design patterns to real-world datasets.
3. Learn strategies for optimizing experimental design to improve the performance and reliability of machine learning experiments.
About ARCH:
The AI Resource Concierge for Health (ARCH) is supported by the National Institutes of Health AIM-AHEAD program.
ARCH Mission:
Support academic and community stakeholders in the adoption of AI/ML tools to advance their health and disease research goals.
ARCH Vision:
ARCH provides expert technical assistance, guidance and collaboration to facilitate adoption of AI/ML tools, supporting the AIM-AHEAD and wider research communities in leveraging these technologies to improve the health of the Nation.
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